Methods and Systems for Analyzing Samples

ABSTRACT

The disclosure features methods for analyzing a sample, the methods including exposing the sample to plurality of pulses of electromagnetic radiation to convert a portion of the sample into a plasma, recording a spectrum of electromagnetic radiation emitted in response to each of the plurality of pulses to define a sequence of spectra for the sample, and using an electronic processor to determine information about the sample based on the spectra, where exposing the sample to the plurality of pulses of electromagnetic radiation includes directing the pulses to be incident on different spatial regions of the sample, and where a temporal delay between exposing the sample to each successive radiation pulse is constant.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 61/790,874, filed on Mar. 15, 2013, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to methods and systems for analyzing samples of materials, and more particularly to methods and systems for determining the identity, place of origin, and/or treatment history of an unknown sample.

BACKGROUND

Provenance determination of materials (e.g., minerals) is useful for a variety of reasons. For example, materials from one location may be more valuable than those from another location. In addition, laws restricting the sale of materials from certain areas may exist due to geopolitical concerns. Currently, distinguishing between particular conflict ore deposits (e.g., columbite and tantalite) requires a combination of mineralogical, geochemical and geochronological analyses, which can be both time consuming and catastrophically destructive to the sample. In the case of rare and highly valuable materials, non-destructive analytical tools are typically necessary to preserve the integrity of the sample.

As it currently stands, non-destructive origin determination is largely based on a combination of human observations and data collected from advanced analytical instrumentation. Final determination decisions typically fall to the uncertain and sometimes varying opinions of research scientists. Techniques traditionally used for origin determination include Raman and Luminescence Spectroscopy, X-ray Radiography, Tomography, Energy-Dispersive X-Ray Fluorescence (EDXRF), and Scanning Electron Microscope Energy-Dispersive Spectroscopy (SEM-EDS). Secondary Ion Mass Spectrometry (SIMS) and Laser-Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) have also recently been applied to provenance determination studies. Each of the above techniques offers both advantages and disadvantages.

SUMMARY

This disclosure is based on the unexpected discovery that the entire emission spectrum of a sample of a physical material (e.g., a gemstone) can be used to determine its place of origin more accurately than a conventional method using only a portion of the emission spectrum of a sample. In addition, this disclosure is based on the unexpected discovery that variations in the emission spectrum of a physical material resulting from sample non-homogeneity and/or different excitation conditions should not be discarded as noise or averaged, but instead can be used to more accurately determine the identity of the sample.

The methods described herein are based on an assumption that every material, natural or man-made, bears traces of the materials and processes involved in its creation. Every sample of material, if examined in sufficient detail, is different from every other sample. The methods described herein use those traces of formation to classify samples according to their similarities and differences.

Provenance determination is useful for a variety of reasons, such as determining the value of the material (e.g., a mineral) or whether a material (e.g., a manufactured material) has been made to its specifications. “Provenance” mentioned herein can refer to geographical sites of discovery in the case of natural materials such as minerals, but can also refer to a particular factory, process, or manufacturer in the case of man-made materials. Provenance determination of man-made materials allows for the identification of counterfeit products and/or substandard products.

In one aspect, this disclosure features a method for analyzing a sample. The method includes (a) converting a portion of the sample into a plasma multiple times; (b) recording a spectrum of electromagnetic radiation emitted in response to each of the sample conversions to define a sequence of spectra for the sample, in which each member of the sequence corresponds to the spectrum recorded in response to a different one of the sample conversions; (c) using an electronic processor to compare the sequence of spectra for the sample to a sequence of spectra for each of multiple reference samples in a reference library; and (d) using the electronic processor to determine information about the sample based on the comparison to the multiple reference samples in the library.

In another aspect, this disclosure features a system for analyzing a sample. The system includes (a) an excitation source for converting a portion of the sample into a plasma multiple times; (b) a spectrometer configured to record a spectrum of electromagnetic radiation in response to each of the sample conversions to define a sequence of spectra for the sample, in which each member of the sequence corresponds to the spectrum recorded in response to a different one of the sample conversions; and (c) an electronic processor configured to compare the sequence of spectra for the sample to a sequence of spectra for each of multiple reference samples in a reference library and determine information about the sample based on the comparison to the multiple reference samples in the library.

Alternatively, in each of these aspects, there can be as a few as one reference sample in the reference library, in which case, the electronic processor can determine the information about the sample based on the comparison to the sequence of spectra in the one reference sample in the library. This is appropriate when the desired information about the sample being analyzed is, for example, a simple verification or authentication that the sample being analyzed does correspond, or does not correspond, to this single reference sample.

Embodiments with respect to any of these four aspects can include one or more of the following features.

In some embodiments, a pulse of electromagnetic radiation is used to convert the sample into the plasma for each of the multiple times. In some embodiments, the pulse of electromagnetic radiation can be derived from a laser, an ion beam, an electron beam, or an arc discharge. For example, the pulse of electromagnetic radiation can be derived from a laser and causes laser-induced breakdown of the sample.

In some embodiments, the sample is a solid (e.g., a gemstone, a metal, a manufactured material, such as a manufactured metal alloy, or a biological material). In certain embodiments, the sample is a liquid (e.g., the sample is blood, urine, oil, or water).

In some embodiments, the one or more reference samples are metal alloys having a common elemental composition and different processing protocols, such as different heat treatments.

In some embodiments, the sample being analyzed and the one or more reference samples can be metal alloys having a common elemental composition, and wherein the information determined by the electronic processor is whether the sample being analyzed has been subjected to a specific processing protocol corresponding to one of the reference samples.

In some embodiments, the conversion of the sample into the plasma causes the sample to emit electromagnetic radiation indicative of atomic emissions. In certain embodiments, the conversion of the sample into the plasma further causes the sample to emit electromagnetic radiation indicative of one or more of isotopic emissions, molecular emissions, molecular isotopic emissions, and spectral interference between atomic emissions from different atoms in the sample.

In some embodiments, each spectrum is recorded with a spectral resolution sufficient to resolve the emission of electromagnetic radiation corresponding to atomic emission and one or more of isotopic emission, molecular emission, molecular isotopic emission, and spectral interference between atomic emissions from different atoms. For example, each spectrum can be measured with a spectral resolution containing at least 10,000 channels. As another example, each spectrum can be measured with a spectral resolution finer than 0.1 nm, and preferably finer than 0.06 nm.

In some embodiments, each spectrum is measured over a range including from 195 nm to 1005 nm.

In some embodiments, members of the sequence for the sample correspond to the spectra recorded in response to different parameters for the pulse of electromagnetic radiation used to convert the portion of the sample into the plasma during the multiple times (e.g., multiple excitations). For example, the different parameters can include different pulse energies, different pulse durations, different pulse wavelengths, or combinations thereof.

In some embodiments, members of the sequence for the sample correspond to the spectra recorded in response to different incident locations on the sample for the pulse of electromagnetic radiation used to convert the portion of the sample into the plasma during the multiple times (e.g., multiple excitations). For example, the different incident locations can be sufficient to characterize heterogeneity in the atomic composition of the sample. In some embodiments, the different locations are separated from one another by at least 10 μm. In some embodiments, the different incident locations include at least 10 different locations (e.g., at least 15 different locations or at least 64 different locations).

In some embodiments, members of the sequence for the sample correspond to the spectra recorded in response to combinations of different parameters for the pulse of electromagnetic radiation used to convert the portion of the sample into the plasma during the multiple times (e.g., multiple excitations) and different incident locations on the sample for the pulse of electromagnetic radiation used to convert the portion of the sample into the plasma during the multiple times.

In some embodiments, the sequence of spectra for the sample can include members corresponding to all of the different spectra recorded for the sample during the multiple times (e.g., multiple excitations). As used herein, each “member” corresponds to a unique spectrum in the sequence of spectra. The set of such members define the “constituent” spectra for the sequence.

In some embodiments, the electronic processor can determine the members of the sequence of spectra for the sample by using a cluster technique. Such analysis can be applied to the sequence of spectra for the sample being analyzed and/or to the sequence of spectra for any of the reference samples.

In some embodiments, the comparison by the electronic processor comprises comparing a probability distribution for the members of sequence of spectra in the sample being analyzed to a probability distribution for the members of the sequence of spectra for each of the reference samples. For example, the probability distribution for the sample being analyzed can be represented as a histogram indicating the number of times each member occurs in the sequence of spectra for the sample being analyzed and the probability distribution for the members of each reference sample can be represented as a histogram indicating the number of times each member occurs in the sequence of spectra for each reference sample.

In some embodiments, the comparison by the electronic processor can include identifying a degree to which the sequence for the sample matches a sequence for each of at least some of the reference samples in the library. For example, identifying a degree can include (a) comparing each spectrum in the sequence for the sample to the different spectra in the library to identify the different spectra from the library most likely to match the spectra in the sequence for the sample; (b) identifying which reference samples from the library comprise all of the identified spectra; and (c) identifying a degree to which the sequence for the sample matches a sequence for each of the identified reference samples. In some embodiments, the electronic processor uses a nearest neighbor algorithm to perform one or both of the above identifying steps.

In another example, identifying the degree to which the sequence for sample matches a sequence for each of the reference samples comprises comparing a probability distribution for the members of the sample being analyzed to a probability distribution for the members of the sequence of spectra for each of the reference samples.

In some embodiments, the reference library is made by (a) providing information about the identity of each reference sample; (b) converting a portion of each reference sample into a plasma multiple times; and (c) recording a spectrum of electromagnetic radiation emitted from each reference sample in response to each of the reference sample conversions to define a sequence of spectra for each reference sample, wherein each member of the reference sample sequence corresponds to the spectrum recorded in response to a different one of the reference sample conversions. For example, members of each reference sample sequence correspond to the spectra recorded in response to combinations of different parameters for a pulse of electromagnetic radiation used to convert the portion of each reference sample into the plasma during the multiple times (e.g., multiple excitations) and different incident locations on each reference sample for the pulse of electromagnetic radiation used to convert the reference sample into the plasma during the multiple times.

In some embodiments, the information about the sample can include an identity or a provenance for the sample.

In a further aspect, the disclosure features methods for analyzing a sample that include exposing the sample to plurality of pulses of electromagnetic radiation to convert a portion of the sample into a plasma, recording a spectrum of electromagnetic radiation emitted in response to each of the plurality of pulses to define a sequence of spectra for the sample, where each member of the sequence corresponds to the spectrum recorded in response to a different one of the pulses, using an electronic processor to compare the sequence of spectra for the sample to a sequence of spectra for each of multiple reference samples in a reference library, and using the electronic processor to determine information about the sample based on the comparison to the multiple reference samples in the library, where exposing the sample to the plurality of pulses of electromagnetic radiation includes directing the pulses to be incident on different spatial regions of the sample, and where a temporal delay between exposing the sample to each successive radiation pulse is constant.

Embodiments of the methods can include any one or more of the following features.

The plurality of pulses of electromagnetic radiation can form a hexagonal array on the sample. The plurality of pulses of electromagnetic radiation can form an array of equally spaced exposure regions on the sample. The exposure regions can be positioned along a circumference of a common circle. The plurality of pulses can include 60 or more pulses.

The methods can include selecting the temporal delay between successive radiation pulses by adjusting a spacing between the different spatial regions of the sample. The methods can include selecting the temporal delay based on the sample. The methods can include selecting a power of each of the plurality of radiation pulses.

The methods can include measuring a first sequence of spectra for the sample corresponding to a first constant temporal delay between successive radiation pulses, and measuring a second sequence of spectra for the sample corresponding to a second constant temporal delay between successive radiation pulses that is different from the first constant temporal delay. The methods can include determining the information about the sample based on the first and second sequences of spectra.

Embodiments of the methods can also include any of the other features or steps disclosed herein, including features and steps disclosed in connection with different embodiments, in any combination as appropriate.

In another aspect, the disclosure features systems for analyzing a sample, the systems including an electromagnetic radiation source configured to expose the sample to plurality of pulses of electromagnetic radiation to convert a portion of the sample into a plasma, a detector configured to record a spectrum of electromagnetic radiation emitted in response to each of the plurality of pulses to define a sequence of spectra for the sample, where each member of the sequence corresponds to the spectrum recorded in response to a different one of the pulses, and an electronic processor configured to: control the radiation source so that the plurality of electromagnetic radiation pulses are incident on different spatial regions of the sample, where a temporal delay between exposing the sample to each successive radiation pulse is constant; compare the sequence of spectra for the sample to a sequence of spectra for each of multiple reference samples in a reference library; and determine information about the sample based on the comparison to the multiple reference samples in the library.

Embodiments of the systems can include any one or more of the following features.

The electronic processor can be configured to control the radiation source so that the plurality of pulses of electromagnetic radiation form a hexagonal array on the sample. The electronic processor can be configured to control the radiation source so that the plurality of pulses of electromagnetic radiation form an array of equally spaced exposure regions on the sample. The exposure regions can be equally spaced along a circumference of a circle. The plurality of pulses can include 60 or more pulses.

The electronic processor can be configured to select the temporal delay between successive radiation pulses by adjusting a spacing between the different spatial regions of the sample. The electronic processor can be configured to select the temporal delay based on the sample. The electronic processor can be configured to control the radiation source to select a power of each of the plurality of radiation pulses.

The electronic processor is configured to use the detector to measure a first sequence of spectra for the sample corresponding to a first constant temporal delay between successive radiation pulses, and use the detector to measure a second sequence of spectra for the sample corresponding to a second constant temporal delay between successive radiation pulses that is different from the first constant temporal delay. The electronic processor can be configured to determine the information about the sample based on the first and second sequences of spectra.

Embodiments of the systems can also include any of the other features disclosed herein, including features disclosed in connection with different embodiments, in any combination as appropriate.

Certain features, aspects, and steps are disclosed herein in connection with particular embodiments. In general, however, those features, aspects, and steps are not particular to those embodiments, and can be combined with other embodiments and other features, aspects, and steps as desired. Accordingly, while particular embodiments have been described herein for purposes of illustration, it should be appreciated that other combinations of the features, aspects, and steps disclosed herein are also within the scope of the disclosure, and that particular embodiments described herein can also include features, aspects, and steps disclosed in connection with other embodiments.

The entire contents of each of the following are incorporated by reference herein: U.S. Provisional Application No. 61/595,903, filed on Feb. 7, 2012; U.S. patent application Ser. No. 13/760,349, filed on Feb. 6, 2013; and PCT Patent Application No. PCT/US2013/024843, filed on Feb. 6, 2013.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the subject matter herein, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the disclosure will be apparent from the description, drawings, and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart showing a series of exemplary steps for comparing a sample to the reference samples in a reference library to obtain information about the sample.

FIG. 2 is a flow chart showing a series of exemplary steps of performing step (c) in FIG. 1.

FIG. 3 is a flow chart showing another series of exemplary steps of performing step (c) in FIG. 1

FIG. 4 is an illustration of an exemplary system for acquiring and analyzing an emission spectrum of a sample.

FIG. 5 shows three graphs showing constituent spectra for sample of 17-4 Stainless Steel subjected to a specific processing condition.

FIG. 6 is a schematic diagram of an analysis system.

FIG. 7 is a schematic diagram of a fiber optic radiation collector.

FIG. 8 is a graph showing scattered light intensity as a function of sample position.

FIG. 9 is a schematic diagram of a hexagonal exposure spot pattern.

FIG. 10 is a schematic diagram of a radial exposure spot pattern.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

In general, this disclosure relates to methods and systems for analyzing a sample (e.g., to determine the identity and/or place of origin of the sample) by using the entire emission spectrum of the sample. In some embodiments, determining the identity of a sample can include determining whether a sample is manufactured according to a certain specification.

In some embodiments, the methods disclosed herein include acquiring one or more emission spectra of an unknown sample (i.e., a sample with an unknown place of origin or an unknown identity) and comparing the acquired spectra with the spectra of each reference sample (i.e., a sample with a known place of origin or a known identity) in a reference library. The place of origin or the identity of the unknown sample can then be determined when its spectra match (i.e., has sufficient similarity to) those of one or more reference samples in the library. In some embodiments, the library can include only one reference sample. In such embodiments, the methods disclosed herein can be used to verify whether the unknown sample is the same as, or different from, the reference sample (e.g., for verification or authentication).)

In general, the samples that can be analyzed by the methods disclosed herein can include any suitable materials, such as a geological material (e.g., minerals, gemstones, rocks, meteorites, or metals), a manufactured material (e.g., machined metal parts, cast metal parts, or pharmaceuticals), or a biological material (e.g., pathogens, bacteria, viruses, foods, or woods). Exemplary minerals include beryl, corundum, tourmaline, diamond, gold, wolframite, cassiterite, and columbite and tantalite (COLTAN). Exemplary gemstones include diamonds, emeralds, rubies, and sapphires. Exemplary rocks include limestones, marbles, and granites. In some embodiments, the samples that can be analyzed by the methods disclosed herein can be an inorganic material (e.g., gemstones) or an organic material (e.g., apples or oranges). In some embodiments, the samples that can be analyzed by the methods disclosed herein are solid samples or liquid samples (e.g., blood, urine, oil, or water).

In one application, the sample being analyzed and the one or more reference samples can be manufactured parts (e.g., metal alloys) that are subject to different processing conditions (e.g., different heat treatments). In such cases, the elemental compositions of the parts may be the same, but the different processing conditions cause the materials to have different properties. The techniques disclosed herein can distinguish between such parts.

FIG. 1 is a flow chart showing a series of exemplary steps for comparing a sample with the reference samples in a reference library to obtain information about the sample. As shown in FIG. 1, the methods disclosed herein can include the following steps: (a) converting a portion of a sample into a plasma multiple times; (b) recording a spectrum of electromagnetic radiation emitted in response to each of the sample conversions to define a sequence of spectra for the sample, in which each member of the sequence corresponds to the spectrum recorded in response to a different one of the sample conversions; (c) using an electronic processor to compare the sequence of spectra for the sample to a sequence of spectra for each of multiple reference samples in a reference library; and (d) using the electronic processor to determine information about the sample based on the comparison to the multiple reference samples in the library.

Step (a) can be performed by irradiating a pulse of electromagnetic radiation to the sample. In general, the electromagnetic radiation has sufficient energy to convert a portion of the sample into a plasma. Exemplary electromagnetic radiations include a laser beam (e.g., a 266 nm, 355 nm, 532 nm, or 1064 nm laser beam), an ion beam, an electron beam, and an arc discharge. Without wishing to be bound by theory, it is believed that the plasma thus formed contains various excited atomic elements, which emit electromagnetic radiations when these atomic elements return to a lower energy state. In some embodiments, the electromagnetic radiations are indicative of atomic emissions. In some embodiments, the electromagnetic radiations can further include those indicative of one or more of isotopic emissions, molecular emissions, molecular isotopic emissions, and spectral interference between atomic emissions from different atoms in the sample.

As used herein, the phrase “atomic emission” refers to emission of an electromagnetic radiation by an atomic element (e.g., a metal element such as Na or Mg) in a sample. Conventionally, atomic emissions have been used as the primary signals in an element analysis measurement to determine the place of origin of a sample, while the other emissions (e.g., isotopic emissions or molecular emissions) have been generally discarded as noise. For example, a conventional method typically selects a part of an emission spectrum (e.g., by using algorithms such as Partial Least Squares (PLS) or Principle Component Analysis (PCA)), or average a number of spectra to reduce what is assumed to be noise. By contrast, the methods disclosed herein rely on the entire emission spectra acquired from a sample to determine its place of origin. Without wishing to be bound by theory, it is believed that the emissions other than atomic emission (e.g., isotopic emissions, molecular emissions, or molecular isotopic emissions) represent “sequences” (or a petrogenetic signature) of a reference or unknown sample and should be included in the data analyses described herein to determine the place of origin of unknown samples. Further, without wishing to be bound by theory, it is believed that the minerals of the same type can have different “sequences” that vary depending on their places of origin (e.g., from countries to countries, from deposits to deposits, from mines to mines, and from zones to zones) and the environmental conditions (e.g., weathering, hydrothermal alternation, and local tectonic stresses) of their places of origin. Thus, without wishing to be bound by theory, it is believed that by using the entire emission spectra acquired from a sample (which can include the above-discussed additional emissions, as well as spectral interferences among these emissions and potentially other yet unidentified features), one can determine the place of origin of an unknown sample more accurately than conventional methods.

As used herein, the phrase “molecular emission” refers to emission of an electromagnetic radiation by a molecule (e.g., H₂O or CO₂) in a sample. The phrase “isotopic emission” refers to emission of an electromagnetic radiation by an isotope of an atomic element (e.g., deuterium and hydrogen, ²³⁵U/²³⁸U, or ¹⁰B/¹¹B) in a sample. Isotopic emissions within spectra are generally small. For example, the isotopic shift between ²³⁵U/²³⁸U at the emission line at 424.412 nm is 0.025 nm. As another example, the isotopic shift between ²³⁹Pu and ²⁴⁰Pu at the emission line of 594.522 nm is 0.005 nm and the isotopic shift between ¹⁰B and ¹¹B at the emission line of 208.889 nm is 0.002 nm. It has been shown by Laser Ablation Molecular Isotopic Spectrometry (LAMIS) that the isotopic shifts found in molecular spectra are significantly larger than those of isotopic (atomic) spectra. For example, the molecular isotopic shift for ¹⁰B¹⁶O and ¹¹B¹⁶O is 0.73 nm, which is significantly larger than the isotopic shift for ¹⁰B and ¹¹B when they are not bonded to O. These isotopic shifts and molecular isotopic shifts are usually so small relative to the total intensity of the emitted radiation from a sample that they are traditionally disregarded as noise. However, as the methods disclosed herein utilize the entire emission spectrum of a sample, these small shifts are retained during data analyses when comparing the spectra collected from an unknown sample to the reference samples in a library.

As used herein, the phrase “spectral interference” refers to incomplete isolation of the radiation emitted by an analyte from other radiations detected by an instrument. As an example, when using the methods disclosed herein to analyze the mineral beryl, the element Be can have spectral interference with V, Ti, Fe, Cr, Mg, and Mn, the element Al can have spectral interference with Mg, V, Ca, Ti, Cr, Fe, and Mn, and the element Si can have spectral interference with Cr, Fe, Mg, V, Al, and Mn. These spectral interferences may cause an inaccurate representation of the chemical composition of the sample tested. Thus, traditionally, spectral interferences can be problematic in analyzing the chemical composition of a sample (especially in a quantitative measurement). By contrast, when used to determine the place of origin of an unknown sample, the methods disclosed herein can utilize the information contained in the spectral interference as these methods involve comparing the entire spectra acquired from the unknown sample with the entire spectra acquired from each of the reference samples in a library, and therefore are not concerned about the absolute intensities of the radiations emitted from the reference and unknown samples.

In general, step (a) in the methods disclosed herein includes irradiating a sample with electromagnetic radiation (e.g., laser) multiple times (e.g., at least 20 times, at least 30 times, at least 40 times, at least 60 times, or at least 80 times). In some embodiments, a sample is irradiated with electromagnetic radiation at multiple locations (e.g., at least 10 locations, at least 15 locations, at least 20 locations, at least 30 locations, at least 40 locations, at least 60 locations, or at least 120 locations) and up to as many as 240 locations or more. In some embodiments, a sample is irradiated with electromagnetic radiation multiple times at each of the above locations (e.g., at least twice or at least three times at each location). Without wishing to be bound by theory, it is believed that a sample (e.g., a mineral) can be heterogeneous both laterally and vertically on a microscopic scale. In addition, different pulses of electromagnetic radiation can have different energy intensities and therefore can produce different emission spectra. Thus, without wishing to be bound by theory, it is believed that by irradiating a sample at multiple locations and multiple times at each of these multiple locations and then collecting the spectra produced by these irradiations, one can capture a more complete picture of the above variations, which are characteristic of the place of origin of a sample. Thus, by using the methods disclosed herein, one can determine the place of origin of a sample more accurately (e.g., pinpointing the particular deposit or mine from which the sample is obtained).

In general, the irradiation locations are spaced from each other at a suitable distance (e.g., to be sufficiently large to characterize heterogeneity in the atomic composition of a sample or to make sure the sample from each location is not contaminated with the debris produced from previous irradiations). In some embodiments, the suitable distance can be at least 10 μm (e.g., at least 15 μm or at least 20 μm). In certain embodiments, the suitable distance can be at least 100 nm (e.g., at least 1 μm or at least 5 μm).

One can generally carry out step (b) by recording a spectrum of electromagnetic radiation emitted in response to each of the sample conversions (i.e., to form a plasma). In some embodiments, each spectrum is first detected by a detector (e.g., a spectrometer) and then recorded in an electronic processor (e.g., a computer). As a sample is irradiated with electromagnetic radiation (e.g., a laser) multiple times (e.g., at least 60 times) in step (a), multiple spectra are obtained from the sample. In general, each spectrum is detected and recorded prior to the next sample conversion by irradiation with electromagnetic radiation. In some embodiments, each spectrum is recorded with a spectral resolution sufficient to resolve the emission of electromagnetic radiation corresponding to atomic emission and one or more of isotopic emission, molecular emission, molecular isotopic emission, and spectral interference between atomic emissions from different atoms. In some embodiments, suitable spectral resolution can be at least 10,000 channels (e.g., at least 20,000 channels, at least 30,000 channels, at least 40,000 channels, at least 60,000 channels, at least 80,000 channels, at least 100,000 channels, at least 200,000 channels, or at least 300,000 channels) and up to as many as 400,000 channels or more. For example, a suitable spectral resolution can be 40,000 or 67,000 channels. Without wishing to be bound by theory, it is believed that using a high spectral resolution in the methods disclosed herein can resolve fine spectral lines or bands and therefore can increase the accuracy of the final results. For example, when a spectral resolution of as many as 400,000 channels is used in a spectral window between 195 nm and 1005 nm, spectral lines or bands having a width of about 2 pm can be resolved.

In some embodiments, after all spectra of a sample are recorded, the spectra can be scaled to a common unit of measurement. The scaling is generally achieved by using a piece of information preserving transformation (e.g., by dividing each spectrum channel by the mean value of the energy used to generate the spectra). The scaled spectra can then be compared among themselves to determine the number of different spectra (also referred to as “constituent signals”). The comparison can be performed (e.g., by an electronic processor such as a computer) as follows: One can first compare first and second scaled spectra from a sample. If these two scaled spectra are sufficiently similar, they are considered to be the same spectrum (i.e., the same constituent signal). If these two scaled spectra are substantially different, they are considered to be two different spectra (i.e., two different constituent signals). In some embodiments, to determine the similarity of two scaled spectra, one can use a matching algorithm (e.g., a weighted K-nearest neighbor algorithm) to compare the entire sequence of spectra for a sample to obtain a common reference spectrum (e.g., a centroid spectrum for all of the spectra for the sample). One can then compute the difference between each spectrum and the common reference spectrum. Subsequently, one can calculate the standard deviation of all of the differences. Two spectra are considered to be similar if the difference between each spectrum and the common reference spectrum is less than a given percent of the standard deviation of the differences from the common reference spectrum. One can then compare the third scaled spectrum with the first two scaled spectra. If the third scaled spectrum is sufficiently similar to one of the first and second scaled spectra, the third scaled spectrum is not considered to be a unique spectrum. If the third scaled spectrum is substantially different from either of the first and second scaled spectra, the third scaled spectrum is considered to be a unique spectrum (i.e., a different constituent signal). The process can be repeated until all scaled spectra collected from a sample have been compared with the other scaled spectra from the same sample. The unique spectra (each of which is in response to a different one of the sample conversions) can then be compiled to form a set of constituent spectra for the sequence. Each unique spectrum in a sequence is also referred to hereinafter as “a member” of the sequence. In general, a sequence can include one member (e.g., if the sample is perfectly homogenous) or two or more members (e.g., if the sample is heterogeneous). For many samples, the sequence typically includes at least 10 members (e.g., at least 15 members or at least 64 members).

Cluster technique algorithms such as those commercially available in MATLAB® toolboxes from MathWorks Inc. (Natick, Mass.) can be used to determine the constituent spectra in a sequence of spectra. For example, the weighted K-nearest neighbor algorithm described above can be used to identify spectra that define a common constituent when the weighted K-nearest neighbor differences are small enough.

In some embodiments, certain members of a sequence for a sample correspond to the spectra recorded in response to different parameters (e.g., different pulse energies, different pulse durations, different pulse wavelengths, or combinations thereof) for the pulses of electromagnetic radiation used to convert a portion of the sample into the plasma during the multiple conversions performed in step (a).

In some embodiments, certain members of a sequence for a sample correspond to the spectra recorded in response to different incident locations on the sample at which the pulse of electromagnetic radiation is irradiated to convert a portion of the sample into the plasma during the multiple conversions performed in step (a). In some embodiments, the different incident locations are sufficient to characterize heterogeneity in the atomic composition of a sample. For example, the different incident locations can be separated from one another by at least 10 μm (e.g., at least 15 μm or at least 20 μm). In some embodiments, certain members of a sequence for a sample correspond to the spectra recorded in response to different parameters for the pulses of electromagnetic radiation and different incident locations.

It is important to note that the methods disclosed herein utilize the entire spectrum of each spectrum in the sequence for a sample (e.g., a reference or unknown sample) without smoothing the spectrum or reducing the noise in the spectra by averaging spectra obtained from different irradiations and/or averaging spectra obtained from different irradiation locations and/or discarding low amplitude spectral lines and bands as noise. Without wishing to be bound by theory, it is believed that all of such information obtained from a sample is important, as it represents the “sequence” of the sample, and can be used to determine the place of origin of an unknown sample more accurately. By contrast, a conventional method generally uses only a portion of a spectrum from a sample, or averages a number of spectra to reduce noise, or discards low amplitude spectral lines as noise, which would lose valuable information about the sample.

After the sequence of spectra for a sample is obtained, step (c) can be performed by comparing the sequence with a sequence of spectra for each of multiple reference samples in a reference library. In general, to determine the place of origin of an unknown sample, the place of origin of each reference sample in the library is known.

Generally, the measured spectra of a sample are also scaled to a common unit of measurement by using a piece of information preserving transformation (e.g., by dividing each spectrum channel by the mean value of the pulse energy used to generate the spectra). This scaling is isomorphic so as to preserve relative variations within each spectrum.

In general, to establish a reference library for a geological material (e.g., a gemstone or a metal), reference samples can first be collected from deposits all around the world. To ensure a high degree of confidence in the place of origin of the geological material, it is desirable to document sufficient information about the material sample during collection, such as (1) the GPS coordinates of the location, (2) the time and date of collection, (3) the name and affiliation of the collector, (4) whether the sample is extracted from weathered rock, (5) whether the sample is extracted directly from a host rock, (6) the zone from which the sample is extracted, (7) the type of host rock, (8) whether the sample is extracted from mine tailings, the floor of the mine, or a river, (9) a description of the physical sample (e.g., its color, size, inclusions, or host rock), and (10) whether the sample is collected with other samples. In some embodiments, it is desirable to collect a statistically significant number of samples (e.g., at least 30 samples) from a particular mine in a deposit. In some embodiments, if a deposit has multiple mines, it is desirable to collect a statistically significant number of samples (e.g., at least 30 samples) from each mine. In some embodiments, if a mine has multiple zones containing the same geological material (e.g., a pegmatite), it is desirable to collect a statistically significant number of samples (e.g., at least 30 samples) from each zone. After collection, all samples are assigned an internal tracking number that can be used to track the samples to the collection event. The documents describing the parameters of collection are preserved with the physical samples, and rigorous chain-of-custody procedures are followed to ensure continuing integrity of the reference collection.

A sequence of spectra of each of the collected reference samples can then be obtained by carrying out steps (a) and (b) described above. In some embodiments, after the spectra of all collected reference samples are obtained, one can then apply a data analysis process (e.g., a matching algorithm such as a weighted K-nearest neighbor algorithm) to the reference samples to determine how similar/dissimilar the reference samples are to each other. In some embodiments, the weighting used in a weighted K-nearest neighbor algorithm is determined by a kernel density estimation function, such as that described in Webb, Statistical Pattern Recognition, John Wiley & Sons, 2002. In some embodiments, the data analysis process takes into account the distance between the data from two irradiation locations, where distance can be measured by the Mahalanobis metric, such as that described in Warren et al., Use of Mahalanobis Distance for Detecting Outliers and Outlier Cluster in Markedly Non-Normal Data, NTIS Technical Report, 2011. Other aspects of the weighted K-nearest neighbor algorithm can be found, for example, in Viswanath, et al., An improvement to k-nearest neighbor classifier, 2011, arXiv: 1301.6324. In some embodiments, the data analysis process can start with a fixed set of test parameters (e.g., the number of data channels, the number of sample groups, the number of samples in each group, the distance between irradiation locations, the number of grid points (irradiation locations) in each sample, the shape of the grid as defined by the number of X-coordinates, y-coordinates and z-coordinates, the weights to be used in the weighting of the K-nearest neighbor algorithm, and/or the percent of the standard deviation of the differences between each spectrum and the common reference spectrum) and then start testing each reference sample at various incident locations on or in the sample. At the completion of this data analysis process, a profile of each test with respect to all of the other tests on or in that sample is defined. After the data analysis process is completed for all reference samples, the data obtained are included in a database. Analysis of the database can then be performed (e.g., by using a matching algorithm such as a weighted K-nearest neighbor algorithm described above) to determine how similar/dissimilar the reference samples are to each other. For example, if test results obtained from the surface of some of the samples are not similar to those obtained from the surface tests of the rest of the samples while test results obtained from a sub-surface in the samples are similar for all of the samples, one can conclude that there are two different types of coatings on the tested samples or that some of the tested samples lack a coating if test results obtained from the sub-surface and surface are similar for that set of samples. Based on the above analysis, samples having a common attribute (e.g., the same place of origin) can be included in a reference group.

In some embodiments, a single sample among a group of samples supposedly from the same place of origin may have very little similarity to the other samples in the group. This can imply that the assumption that this sample shares a common attribute with the other samples in the group may be false and may require further investigation before including this sample in a reference group. For example, the above incident can be caused by a human error (e.g., by misplacing a sample from a different location in that group).

In some embodiments, the above data analysis process can be applied to different reference groups in a reference library to determine the inherent dissimilarity between the groups. In general, no assumptions are made about the size or number of the reference groups that make up the reference library other than that each reference group has some attribute(s) that make it distinct from other groups. For example, the reference library can include only two reference groups, with one group having reference samples with a desirable property and the other group having reference samples lacking that desirable property. As another example, a reference library can include a large number of reference groups. For example, a reference library can be composed of emerald samples that are grouped based on the countries they came from.

In some embodiments, after a reference library is created, the sequence of spectra of an unknown sample can be compared with the sequence of spectra for each of the reference samples in the reference library (e.g., by using a data analysis process such as a weighted K-nearest neighbor algorithm) to determine whether the sequence of the unknown sample is similar to those of the samples in a reference group in the library. Suitable data analysis processes are discussed above. Based on the comparison, step (d) can be performed by using an electronic processor (such as a computer) to determine certain information about the sample. For example, if the sequence of the unknown sample is substantially similar to the sequence of one or more samples in a particular reference group, it can be concluded that the unknown sample belongs to this group. As another example, if the sequence of the unknown sample is significantly different from the samples in all of the reference groups in a library, it can be concluded that the unknown sample belongs to a new group not already in the library.

As noted above, in certain embodiments, the reference library can include only one reference sample. In such embodiments, the methods disclosed herein can be used to verify whether the unknown sample is the same as, or different from, the reference sample (e.g., for verification or authentication applications).

In some embodiments, after a reference library composed of mineral samples grouped based on their places of origin is created, the place of origin of an unknown sample of the same mineral can then be determined by comparing its sequence with the sequence of spectra for each of the reference samples in the reference library. In some embodiments, the comparison can be performed (e.g., by an electronic processor) to identify a degree to which the sequence of the sample matches a sequence for each of at least some of the reference samples in the library. For example, FIG. 2 is a flow chart showing a series of exemplary steps of performing this comparison. As shown in FIG. 2, the comparison can be performed by (a) comparing each spectrum in the sequence for the sample to the different spectra in the library to identify the different spectra from the library most likely to match the spectra in the sequence for the sample; (b) identifying which reference samples from the library include all of the identified spectra; and (c) identifying a degree to which the sequence for the sample matches a sequence for each of the identified reference samples. The above comparing and identifying step can be performed by using a data analysis process (e.g., the weighted K-nearest neighbor algorithm described above).

In some embodiments, the comparison by the electronic processor between the sequence of spectra for the sample being analyzed and the sequence of spectra for each of the reference samples includes comparing a probability distribution for the members of sequence of spectra in the sample being analyzed to a probability distribution for the members of the sequence of spectra for each of the reference samples. For example, the probability distribution for the sample being analyzed can be represented as a histogram indicating the number of times each member occurs in the sequence of spectra for the sample being analyzed and the probability distribution for the members of each reference sample can be represented as a histogram indicating the number of times each member occurs in the sequence of spectra for each reference sample. The electronic processor can then determine whether the sample being analyzed is one of the reference samples based on the degree to which this probability distribution for the sample being analyzed substantially matches this probability distribution for any one of the reference samples.

This algorithm is shown schematically in FIG. 3 according to the following steps: (a) construct a probability distribution for the members of the sequence of spectra for the sample being analyzed; (b) for each reference sample in the library, construct a probability distribution for the members of the sequence of spectra for that reference sample; (c) compare the probability distribution for the sample being analyzed to the probability distribution for each of the reference samples in the library; and (d) identifying a degree to which the probability distribution for the sample being analyzed is similar to the probability distribution for any of the reference samples.

Without wishing to be bound by theory, it is believed that each mine or deposit has a unique petrogenetic signature, i.e., elemental and isotopic ratios unique to the petrogenesis of the deposit, and that reference samples from the same mine or deposit have a similar petrogenetic signature. Further, without wishing to be bound by theory, it is believed that one advantage of the methods disclosed herein is that the entire emission spectrum (including atomic emissions, isotopic emissions, molecular emissions, molecular isotopic emissions, and spectral interference between atomic emissions from different atoms) in response to each irradiation of a sample with electromagnetic radiation (e.g., laser) is utilized in the above data analysis to determine the similarity/differences between the samples in a library since only the entire emission spectrum of a sample can include all of the information in the petrogenetic signature of the sample. As a result, the methods disclosed herein can create a reference library containing reference samples with more precise location information and can identify the place of origin of an unknown sample more accurately than conventional methods, which typically use a selected window of spectrum, use atomic emissions only, or use averaged spectra to identify the place of origin of an unknown sample.

FIG. 4 illustrates an exemplary system of acquiring and analyzing an emission spectrum of a sample, which can be used to perform the methods discussed above (e.g., creating a reference library of various reference samples or determining the place of origin of an unknown sample). As shown in FIG. 1, system 10 includes a sample 20, an excitation source 30, a detector 40, and an electronic processor 50. Sample 20 can be those described above. Examples of excitation source 30 can be a laser (e.g., a Nd:YAG laser), an ion beam source (e.g., a liquid-metal ion source), an electron beam source, or an arc discharge lamp. Detector 40 can be a spectrometer (e.g., an Echelle spectrometer).

Although FIG. 4 depicts a certain geometric arrangement for excitation source 30 and detector 40 relative to sample 20, this is only by way of example. Accordingly, many different arrangements are possible for the relative positioning of excitation source 30, detector 40, and sample 20 as long as system 10 can acquire the emission spectra of sample 20. For example, in some embodiments, excitation source 30 can be placed at a location so that the incident irradiation is at a 90° angle to sample 20 and the emission from the sample is collected at a 45° angle from sample 20.

Electronic processor 50 can include one or more programmable computers and/or preprogrammed integrated circuits. It can further include one or more data storage systems (e.g., a memory and/or a storage element), one or more input devices (e.g., a keyboard), and one or more output devices (e.g., a display or a printer). Electronic processor 50 is generally designed to execute programs based on standard programming techniques. System 10 can also include other components (not shown in FIG. 1), such as a sample holder or a sample stage (with the capability of three-dimensional movement) and a camera (e.g., an ICCD camera). Furthermore, in some embodiments, some or all of the components of electronic processor 50 are directly coupled to detector 40. In other embodiments, some or all of the components of electronic processor 50 are physically separated from detector 40. For example, some or all of the processing can be carried out among one or more distributed processors that are located far from detector 40 (e.g., in the “cloud”).

In some embodiments, the methods discussed above can be carried out by first emitting a pulse of electromagnetic radiation 12 (e.g., a high power laser pulse) from excitation source 30 to irradiate an incident location on sample 20 to create a plasma 16, which includes excited atomic elements. This step can be performed at multiple incident locations and/or multiple times at each incident location. Electromagnetic radiation 14 emitted from plasma 16 can then be collected (e.g., through fiber optics or a collimating lens) and detected by detector 40. The signals received from detector 40 can then be forwarded to electronic processor 50 to be recorded as emission spectra of the sample and analyzed (as described above) to determine the place of origin of the sample (e.g., by comparison to the emission spectra of reference samples in a reference library).

In some embodiments, the system shown in FIG. 1 can be a laser-induced breakdown spectroscopy (LIBS) system. In such embodiments, excitation source 30 can be a laser. In general, the laser has a sufficiently high energy to convert a portion of sample 20 to plasma 16. In some embodiments, the laser has a pulse energy of at least about 10 mJ (e.g., at least about 12 mJ, at least about 14 mJ, or at least about 16 mJ) and/or at most about 250 mJ (e.g., at most about 200 mJ, at most about 180 mJ, at most about 160 mJ, at most about 140 mJ, at most about 120 mJ, at most about 100 mJ, at most about 80 mJ, at most about 60 mJ, at most about 40 mJ, or at most about 20 mJ). In some embodiments, the pulse energy for each irradiation to sample 20 is substantially the same.

In some embodiments, the laser has a pulse duration of about 0.1 ps (e.g., at least about 1 ps, at least about 10 ps, or at least about 100 ps) and/or at most about 10 ns (e.g., at most about 5 ns, at most about 1 ns, or at most about 0.5 ns). In some embodiments, the pulse duration for each irradiation to sample 20 is substantially the same.

In some embodiments, a LIBS system can include two lasers with different wavelengths. For example, a LIBS system can include a 266 nm laser and a 1064 nm laser. Without wishing to be bound by theory, it is believed that the 266 nm laser can be used for analyzing transparent samples as it minimizes the traces or damage of testing to the samples and the 1064 nm laser can be used for analyzing translucent and opaque samples as it couples better with the surface of such a sample.

In some embodiments, a LIBS system can include a detector (e.g., a spectrometer) with sufficiently high spectral resolution and sufficiently wide spectral window. In some embodiments, the detector has at least 10,000 channels (e.g., at least 20,000 channels, at least 30,000 channels, at least 40,000 channels, at least 60,000 channels, at least 80,000 channels, at least 100,000 channels, at least 200,000 channels, or at least 300,000 channels) and up to as many as 400,000 channels or more. In some embodiments, the detector can have 40,000 or 67,000 channels. In some embodiments, the detector can resolve features or peaks finer than 0.1 nm (e.g., finer than 0.06 nm or finer than about 0.02 nm). In some embodiments, the detector can have a spectral window between 195 nm and 1005 nm. For example, with as many as 400,000 channels, the spectral resolution is finer than about 2 pm over the spectral window between 195 nm and 1005 nm.

Other components in a LIBS system are generally known in the art, such as those described in U.S. Pat. Nos. 5,751,416; 7,195,371; and 7,557,917; Cremers et al., Handbook of Laser-Induced Breakdown Spectroscopy, John Wiley & Sons Ltd, 2006; and Miziolek et al., Laser-Induced Breakdown Spectroscopy (LIBS) Fundamentals and Applications, Cambridge University Press, 2006.

In general, LIBS systems offer various advantages over other analytical techniques for determining the place of origin of an unknown sample. For example, LIBS systems are easy to use (e.g., requiring minimal sample preparation) and relatively inexpensive. In addition, LIBS systems can be portable so they can be used outside of a laboratory (e.g., at a field site). LIBS systems are available from commercial sources, such as Photon Machines, Inc. (Redmond, Wash.) and Applied Spectra (Fremont, Calif.).

In some embodiments, a LIBS system can be used to analyze a reference or unknown sample as follows. Prior to a sample being analyzed, the laser in the LIBS system is generally warmed up (e.g., by irradiating pulses of laser without using the emitted radiation for analysis) for a sufficient period of time (e.g., at least 10 minutes). After the laser is warmed up, a calibration sample can be analyzed to ensure the repeatability of results (e.g., from day to day). All data collected on calibration samples can be saved along with photos of the calibration sample. If the analysis of the calibration sample does not fall within tolerated levels, a diagnostic test can be performed to ensure the LIBS system is working correctly. If the analysis of the calibration sample falls within tolerated levels, the analysis of a reference or unknown sample can begin.

In general, sample 20 is cleaned prior to being analyzed by a LIBS system. For example, sample 20 can be cleaned by a medical alcohol wipe, and followed by washing with an alcohol (e.g., isopropyl alcohol). After sample 20 is cleaned, it can be mounted on a sample stage in a mounting material (e.g., a mineral tack) for testing in a LIBS system. The mounting material is generally changed when a different sample is analyzed to reduce the risk of cross contamination.

Once a sample is cleaned and mounted, it can be brought into focus on the sample stage. In some embodiments, when the sample is optically in focus on a computer monitor connected to a camera in the LIBS system, it is also in focus for the laser beam to form a plasma. Prior to irradiating a laser pulse to the sample to generate a plasma, a flow of a high purity inert gas (e.g., 99.999% pure argon) can be turned on to cover the surface of the sample to be analyzed. Without wishing to be bound by theory, it is believed that, due to extremely small variations present in geological materials, using a pure inert gas (e.g., argon) to cover the sample surface can avoid contamination and variability from atmospheric air.

In certain embodiments, system 10 includes an adjustable iris to control the spatial profile of radiation 12 produced by source 30. For example, the iris can be used to adjust a beam diameter of radiation 12, spatially filtering certain portions of the beam to generate a filtered output beam. FIG. 6 shows a schematic diagram of system 10 that includes an adjustable iris 60 which is coupled to electronic processor 50. As shown in FIG. 6, iris 60 filters radiation 12 before the radiation is incident on sample 20.

During use, system 10 can be configured to automatically correct the position of iris 60 if the iris falls out of alignment with source 30. Correction of the position of iris 60 can be initiated by electronic processor 50 upon detection of a triggering event, and/or as part of a routine calibration of system 10, which can occur at periodic intervals. In either circumstance, electronic processor 50 is typically configured to determine whether adjustment of iris 60 is needed based on measurement of emitted radiation 14 from sample 20.

For purposes of calibration, in some embodiments, system 10 can include a dedicated calibration sample. In FIG. 6, for example, a calibration sample 20 is mounted to a support 55, which is connected to processor 50 via control line 57. To initiate calibration of system 10, processor 50 delivers a control signal to support 55 which causes support 55 to position the calibration sample in the path of radiation 12 from source 30. Where system 10 includes an automated sample positioning system (as will be described later), processor 50 can also deliver a control signal if necessary to the automated sample positioning system to withdraw any sample from the path of radiation 12.

Support 55 can generally be implemented in a variety of ways. In some embodiments, for example, support 55 is implemented as an articulating arm on which the calibration sample is mounted. In some embodiments, support 55 is implemented as a rotating disc that includes a solid portion on which the calibration sample is mounted, and a large aperture. During calibration, the disc is rotated so that the calibration sample is positioned in the path of radiation 12. During sample measurements, the disc is rotated so that the aperture allows radiation 12 to pass through the disc and be incident on the sample.

System 10 can generally be configured to perform calibration under a variety of conditions. In some embodiments, for example, system 10 performs a calibration routine upon system start-up and/or upon system shutdown. In certain embodiments, system 10 performs a calibration routine after having been in operation for a certain time period and/or after having analyzed a certain number of samples. In some embodiments, a calibration routine can also be performed when positions of any of the system components have changed, when the sample has changed, and/or upon receiving a signal from a system user.

To check whether the alignment of iris 60 is within tolerance, electronic processor 50 typically delivers 64 laser shots in an 8×8 grid pattern to the surface of sample 20, and measures (using detector 40) radiation 14 emitted following each shot. Processor 50 then calculates the average maximum signal intensity for the emitted radiation corresponding to the 64 shots, and compares the average maximum signal intensity to a threshold intensity value. If the average maximum signal intensity falls below the threshold value, processor 50 determines that the alignment of iris 60 should be checked.

To check the alignment of iris 60, processor 50 delivers a control signal to an actuator within iris 60 through control line 62. The control signal causes the actuator to displace iris 60 from its original position by a fixed amount along a first axis. Then, processor 50 activates source 30 to deliver an additional 8×8 pattern of 64 laser shots to sample 20, measures emitted radiation 14 following each shot, and calculates the average maximum signal intensity for the 64 shots. If the average maximum signal intensity has increased relative to the prior measurement, processor 50 delivers another control signal to the actuator within iris 60 to displace the iris in the same direction. If instead the average maximum signal intensity has decreased relative to the prior measurement, processor 50 delivers another control signal to the actuator to displace the iris in the opposite direction. This process of measurement and displacement is repeated until the iris location along the first axis at which the average maximum signal intensity is highest is determined. Once determined, processor 50 positions iris 60 at this location.

In certain embodiments, processor 50 repeats this iterative alignment check for a second axis, which is typically perpendicular to the first axis (and the first and second axes are typically both orthogonal to a propagation direction of radiation 12 produced by source 30). In this manner, iris 60 can be positioned or re-positioned at an optimized location in a plane transverse to the propagation direction of radiation 12.

The threshold value of the average maximum signal intensity that is used to determine whether the alignment of iris 60 should be checked depends upon a variety of factors, including the configuration of detector 40 and the nature of sample 20. In some embodiments, for example, the threshold value corresponds to between 123,000 and 128,000 detector counts.

In some embodiments, processor 50 is configured to also check the diameter of iris 60. The diameter of iris 60 can be checked based on the same triggering event (e.g., a measurement of average maximum signal intensity that falls below an established threshold) that triggers a check of the iris alignment. For example, after optimizing the location of iris 60, processor 50 can deliver a control signal to a second actuator in iris 60 that causes the actuator to increase the diameter of iris 60. Processor 50 then activates source 30 to deliver a further 8×8 pattern of 64 laser shots to the surface of sample 20, calculates an average maximum signal intensity corresponding to the 64 shots, and compares the average maximum signal intensity to an established target range for the signal intensity. If the calculated intensity is larger than the target range, processor 50 delivers a control signal to the second actuator causing it to reduce the diameter of iris 60. If the calculated intensity is smaller than the target range, processor 50 delivers a control signal to the second actuator causing it to increase the diameter of iris 60. Processor 50 then repeats the steps described above in an iterative manner until the calculated average maximum signal intensity falls within the target range, thereby ensuring that the diameter of iris 60 falls within established tolerances. In certain embodiments, system 10 is also configured to check the alignment and/or condition of various optical elements in the system. For example, referring to FIG. 6, system 10 can include an objective lens 70 positioned in a mount with actuators that displace lens 70 in two orthogonal directions in a plane transverse to the propagation direction of radiation 12. As part of a calibration procedure and/or after a triggering event, processor 50 can be configured to check the alignment of objective lens 70.

The procedure for checking the alignment of lens 70 is similar to the procedure for checking the alignment of iris 60. As described above, an example triggering event can be measurement of an average maximum signal intensity for an 8×8 grid pattern of laser shots that falls below a threshold value (e.g., the same threshold value that triggers the check of the iris alignment). Typically, processor 50 first checks and optimizes alignment of iris 60. Then, processor 50 checks and optimizes the alignment of lens 70 in a similar manner, first along one transverse axis, and then along the other.

In certain cases, after alignment of both iris 60 and lens 70, the calculated average maximum signal intensity may still fall below the threshold value. This can occur, for example, if lens 70 is contaminated or damaged. When this condition occurs, processor 50 can deliver a visual and/or audio warning to a user of system 10 that the measured signal level is out of tolerance, and that the optical components of the system (e.g., lens 70) should be visually inspected.

To further protect the optical elements of system 10 against contamination and damage, and to reduce the effects of environmental conditions (e.g., humidity, temperature fluctuations, dust) on the measurement results, some or all of the system optics can be enclosed within a housing. Referring again to FIG. 6, in some embodiments, system 10 can include a housing 80 that extends along a portion, or all, of the path of radiation 12. In general, housing 80 can enclose some or all of the components of system 10, and can be fixed or can be movable with respect to the other components of system 10.

For example, in FIG. 6, housing 80 is a movable housing that encloses iris 60 and lens 70. Housing 80 is connected to an actuator 84, which is coupled to electronic processor 50 via control line 86. Processor 50, by delivering suitable control signals to actuator 84, can extend and retract housing 80 in a direction parallel to arrow 88. For example, during positioning of sample 20, housing 80 is typically in a retracted position. Once the position of sample 20 has been fixed, housing 80 is then extended by processor 50 until the housing is close to, or even contacts, the surface of sample 20. A sensor 89 mounted on housing 80 and coupled to processor 50 alerts processor 50 when housing 80 contacts the surface of sample 20, so that processor 80 halts extension of housing 80. Housing 80 can also include an opening 82 that allows emitted radiation 14 from sample 20 to exit the housing and be detected.

Housing 80 can generally be formed from a variety of different materials, such as various plastics. It can be advantageous for certain applications if housing 80 is formed from a material that is partially or completely transparent. Accordingly, in some embodiments, housing 80 can be formed from a clear plastic material such as Lucite®.

In some embodiments, detector 40 can include an adjustable fiber optic collector for collecting emitted light 14 from sample 20. Samples 20 can typically be of significantly different size, and proper positioning of sample 20 with respect to detector 40 is an important consideration. Further, the alignment of detector 40, including its fiber optic collector, can change due to a variety of factors including mechanical vibrations, heating and cooling cycles, changes in ambient humidity, and accidental handling errors.

FIG. 7 shows a schematic diagram of detector 40 and fiber optic collector 90. To permit reproducible positioning and re-positioning of fiber optic collector 90, system 10 can include an actuator 98 coupled to processor 50 that positions fiber optic collector 90 with respect to sample 20. In general, processor 50 can be configured to position fiber optic collector 90 using the same iterative procedure described above in connection with the optimization of iris 60 and lens 70. In general, the position of fiber optic collector 90 is optimized in three-dimensional space with respect to the position of sample 20, using the calculation of average maximum signal intensity as feedback to guide the iterative adjustment of fiber position. In some embodiments, fiber optic collector 90 can include a sensor 99 that alerts processor 50 when fiber optic collector 90 contacts sample 20 (or housing 80), so that the process of positioning collector 90 does not damage or cause mis-alignment of collector 90 or other system components.

As discussed above in connection with iris 60 and lens 70, the position of collector 90 can be checked at certain intervals (e.g., as part of a regular calibration routine) and/or when a triggering event is detected, such as a low measured value of average maximum signal intensity. In addition, collector 90 can be re-positioned when sample 20 is changed. In general, when re-positioning is performed as part of a calibration routine or as a result of a triggering event, the position of collector 90 is checked and adjusted before the positions of iris 60 and/or lens 70 are checked.

In certain embodiments, fiber optic collector 90 can include multiple ports. For example, collector 90 in FIG. 7 includes a measurement port 92 and a calibration port 94 joined together at fiber splice 95. Measurement port 92 collects emitted radiation 14 from sample 20, as discussed above. Calibration port 94 is coupled to calibration source 93, e.g., a mercury vapor lamp. When the spectral response of detector 40 is being calibrated by system 10, processor 50 activates calibration source 93, which generates calibration light that is coupled into detector 40 through calibration port 94. When system 10 is measuring emitted radiation 14 from sample 20, the emitted radiation is coupled into detector 40 through measurement port 92.

The use of multiple fiber optic ports in fiber optic collector 90 can have important advantages. For example, by using different ports for calibration and measurement of emitted radiation, calibration source 93 does not have to be mounted and de-mounted from collector 90. The process of mounting and de-mounting calibration source 93 is time consuming, prone to causing fiber mis-alignment, and can potentially weaken the connections between components even via normal handling. By fixing the connection between calibration source 93 and calibration port 94, these factors are significantly mitigated.

In general, collector 90 can include any number of fiber ports. Additional fiber ports can be used, for example, to connect other components to detector 40, such as other calibration sources. Alternatively, or in addition, collector 90 can include more than one measurement port in some embodiments.

Other arrangements can also be used to couple light from calibration source 93 into detector 40. For example, in certain embodiments, light from calibration source 93 can be coupled into detector 40 using one or more mirrors and/or beamsplitting elements and/or partial reflectors. These elements can be position either internal or external to a housing enclosing detector 40. Light from calibration source 93 can thereby be directed along either a portion of the same optical path traveled by emitted light 14, or along a different optical path, e.g., along a parallel optical path, to reach the active element(s) of detector 40.

In some embodiments, system 10 can include an automated sample positioning system 100. Positioning system 100 can be implemented in a variety of ways. In certain embodiments, for example, positioning system 100 includes a three-axis stage, with actuators configured to translate sample 20 independently along each of three orthogonal coordinate axes. Processor 50 delivers control signals to positioning system 100 through control line 102 that cause system 100 to translate sample 20. Measurement of average maximum signal intensity, described above, can be used as a feedback mechanism to determine when sample 20 has been properly positioned. Sensors 89 and 99 provide feedback signals to processor 50 to ensure that sample 20, during the positioning process, does not contact housing 80 or fiber optic collector 90.

In certain embodiments, positioning system 100 can be implemented as a movable conveyor system for translating samples rapidly into position with respect to system 10. In such a configuration, which is particularly amenable to similarly-sized and -shaped samples (e.g., when system 10 is used in a manufacturing environment), the various components of system 10 (e.g., fiber optic collector 90, housing 80) remain relatively fixed in position, while individual samples are positioned relative to these fixed components. Following minor adjustment of the positions of the components of system 10, the sample is analyzed and then transported away by positioning system 100, which then introduces another sample of similar dimensions.

In some embodiments, during the analysis process, a certain number (e.g., at least four) of pictures of the sample can be taken. For example, one can take two pictures of the sample before analysis and two pictures after analysis. In some embodiments, prior to the analysis of the sample, one can take a picture of the sample in focus on the sample stage and label it as a “before” picture without a “grid.” After selecting an area on the sample to be analyzed and superimposing a grid on the sample but prior to analyzing the sample, one can then take a second picture of the sample (which is still in focus and sitting on the sample stage) and label it as a “before” picture with a “grid.” Without wishing to be bound by theory, it is believed that it can be important to take pictures prior to sample analysis as a sample may have very small variations (e.g., inclusions or surface irregularities) that may be completely converted into a plasma upon irradiation with a laser. In that case, the variations may produce anomalies in the emission spectra of the sample, which can be explained by comparing the pictures taken before and after sample analysis.

In certain embodiments, system 10 can be configured to automatically take and save the pictures described above. Automatic capture and storage of such pictures relieves the system operator from the burden of performing this task manually, and ensures that pictures are stored in the same location with systematic naming conventions, and excludes the possibility that the system operator will forget to capture or store the images. In some embodiments, detector 40 can be used to capture the images described above. Alternatively, in certain embodiments, system 10 can include another camera that can be used to automatically capture the images.

In some embodiments, based on the geological material being analyzed, one can select the spacing between irradiation or incident locations on a sample. The irradiation of a sample by a laser that produces a plasma is also known as a “shot.” In some embodiments, the spacing between shot locations can be at least 10 μm. However, this spacing can increase (e.g., to at least 15 μm or at least 20 μm), for example, based on how a sample responds to the conversion from a solid to a plasma. For example, when the sample is gold, the spacing between shot locations is often more than 250 μm as the gold can be completely ablated for a diameter of about 200 μm. In certain embodiments, the spacing can be decreased to at least 100 nm (e.g., at least 1 μm or at least 5 μm).

In certain embodiments, system 10 can be configured to use a variety of different spot patterns for illuminating sample 20. As described above, commonly used illumination patterns include line patterns and square grid patterns. More generally, however, other patterns can also be used. In particular, system 10 can allow a system operator to define specific patterns of illumination spots for use in illuminating sample 20.

It has been observed that when the shot-to-shot interval between successive laser shots is not constant, measured emitted radiation includes signal components that reflect the non-constant intervals. FIG. 8 shows a graph of measured signal intensity for an 8×8 square grid pattern of 64 laser shots on the surface of a sample. As shown in FIG. 8, the measured signal intensity for the first shot of each row of the grid is markedly lower than the measured signal intensity for other shots in the grid. The time delay between the last shot in one of the grid rows and the first shot in the next grid row is longer than the time delay between successive shots in any of the grid rows due to longer translational motion of source 30. The effects observed in FIG. 8 have been consistently recognized by the inventors for certain classes of samples and for certain shot grid patterns.

To reduce and/or eliminate the effects of non-constant delay times between all shots in an exposure pattern, exposure patterns in which the delay from shot-to-shot is consistent among all shots in the pattern can be used. In general, a variety of such exposure patterns can be implemented. For example, FIG. 9 shows a hexagonal shot pattern, with steps between successive shots in the pattern indicated by arrows. The distance of travel between each shot is the same in the pattern of FIG. 9, so that the time delay between each shot is also the same.

FIG. 10 shows another shot pattern with successive shots positioned along the circumference of a circle. Between shots, displacement occurs from one side of the circle to the other, passing close to (but not through) the circle's center. The arrows in FIG. 10 indicate the direction of translation after each shot. By moving along a path that is displaced slightly from the center of the circle, each shot is displaced along the circumference of the circle, but the displacement (and time delay) between successive shots is the same. The density of shots along the circumference of the circle can be adjusted by adjusting the displacement of the path of translation from the center of the circle.

More generally, a variety of different shot patterns can be used. As disclosed above, shot patterns in which the delay time between successive shots is the same among all shots can be used to reduce or eliminate the measured effects shown in FIG. 8. However, shot patterns with non-constant delay times between certain successive shots, including user defined shot patterns with this characteristic, can also be implemented.

In some embodiments, prior to irradiating laser pulses to a sample, other analytical parameters (e.g., laser wavelength, laser power, or spectral delay) in a LIBS system can be set. Based on the geological material analyzed, these parameters can be varied slightly to capture the petrogenetic signature of the sample. In some embodiments, prior to selection of analytical parameters, an analytical parameter determination test can be performed. This test can include analyzing multiple samples of the same mineral from different locations. The variable analytical parameters can be changed (e.g., one at a time) to evaluate a large number (e.g., as many as 300) of different permutations before a final set of analytical parameters is established for the mineral.

As an example, when the mineral beryl is analyzed, one can use a LIBS system having a 266 nm laser, a laser power of 90% (which corresponds to between 12 and 20 mJ), a spectral delay of 1.5 μs, and a gain of 150 μs. All of the analytical parameters used during the analysis process can be documented, including the size of the superimposed grid (e.g., 2 excitation locations by 5 excitation locations) and the location of the initial shot. Based on the movement allowed by the LIBS system used in this embodiment, all analyses can be collected at irradiation locations from left to right and then from top to bottom. The collection of the irradiation locations can be important as it helps to determine the number of constituent signals in a sample. In some embodiments, the depth of analysis can vary greatly based on the laser power used. However, the depth of analysis can stay relatively constant within a set of selected analytical parameters.

In general, once all of the analytical parameters of a LIBS system are set, the methods disclosed herein can be performed to analyze a reference or unknown sample.

In some embodiments, multiple detectors can be used to measure emitted light from sample 20 that corresponds to different combinations of exposure and detector parameters. Parameters that are typically varied during measurement of sample data include shot-to-shot delay times, laser power, detector gain settings, and the spacing between laser shots on the sample surface. Among these, shot-to-shot delay times and the power delivered in each shot are particularly amenable to variation and, when varied, provide valuable information that can be used to characterize samples.

To measure emitted radiation from sample 20 that corresponds to different combinations of exposure and detection parameters, system 10 can include multiple detectors. For example, detector 40 can include multiple detection elements (e.g., multiple spectrometers), each of which is dedicated to measuring emitted radiation that corresponds to a different combination of parameters. As an example, a first spectrometer can be configured to detect emitted radiation corresponding to an exposure pattern with a first shot-to-shot delay time, and a second spectrometer can be configured to detect emitted radiation corresponding to an exposure pattern with a second shot-to-shot delay time.

Multiple detectors can be implemented in system 10 in a variety of configurations. In some embodiments, for example, detector 40 can include a housing that encloses multiple detection elements, such as multiple spectrometers as discussed above. Within the housing, optical elements such as beamsplitters are positioned to direct portions of the emitted radiation from sample 20 to each of the detection elements. The optical elements can be fixed in position, so that each detection element receives a portion of the emitted radiation. Alternatively, the optical elements can be movable so that the emitted radiation is selectively directed to one of the detection elements.

Alternatively, in some embodiments, system 10 can include multiple self-contained detectors. The detectors can be self-contained spectrometers, for example. Light emitted from sample 20 can be collected by fiber optic collector 90, which can include multiple distribution ports, each connected to one of the self-contained detectors. In such a configuration, each of the detectors receives a portion of the emitted radiation through a corresponding fiber distribution port. The distribution ports are spliced to the fiber optic collector 90. As a further alternative, the output end of fiber optic collector 90 can be positioned in proximity to one or more optical elements that direct portions of the emitted radiation to the detectors. The optical elements can be fixed in position, so that each detector receives a portion of the emitted radiation, or the optical elements can be movable so that the emitted radiation is selectively directed to one of the detectors, as described above.

In some embodiments, system 10 includes a gas delivery system. Referring again to FIG. 6, system 10 can include a gas source 104 connected to a delivery tube 106 with a nozzle. Gas source 104, which can include one or more actuators, is connected to processor 50 via control line 107. Gas source 104 delivers one or more gases (e.g., argon) to a location in the vicinity of the spot where radiation 12 is incident on the surface of sample 20. Gas delivery tube 106 can be formed from a variety of materials including, for example, plastics.

In certain embodiments, processor 50 can be configured to optimize the position of gas delivery tube 106. For example, by calculating average maximum signal intensity for a shot pattern on the surface of a sample as described above, processor 50 can determine whether the position of gas delivery tube 106 should be checked. Alternatively, or in addition, the position of gas delivery tube 106 can checked as part of a normal calibration routine for system 10.

To check and/or optimize the position of gas delivery tube 106, processor 50 can follow a procedure similar to the one described above for iris 60. In general, the actuators in gas source 104 are configured to position source 104 and delivery tube 106 in three dimensional space. Accordingly, processor 50 iteratively re-positions source 104 and delivery tube 106 in three dimensions until an optimum position (e.g., a position that satisfies a threshold condition and/or produces the highest measured signal) is found. Typically, the position of gas delivery tube 106 is checked and/or adjusted only after adjustment of the positions of the various optical components in system 10.

In some embodiments, system 10 can include one or more environmental sensors for measuring a variety of conditions relevant to the measurement results. Referring to FIG. 6, system 10 includes a sensor 110 that is configured to measure one or more of temperature, barometric pressure, and humidity. In certain embodiments, system 10 includes separate sensors for measuring some or all of these quantities.

In certain embodiments, system 10 can include one or more vibration sensors. In general, low frequency mechanical vibrations can produce artifacts in measurement results that are actively being accumulated, and can also displace various components of system 10 from their proper position, thereby affecting future measurement results. Accordingly, system 10 can include a vibration sensor 120 that detects and reports vibrations to processor 50. In response to the detection of vibrations, processor 50 can initiate a number of actions. For example, processor 50 can initiate a calibration routine to ensure that the various components of system 10 remain properly aligned. The calibration routine can include any of the alignment steps disclosed herein. As another example, processor 50 can deliver a warning (e.g., a visual and/or audio message) to a system operator that data being currently acquired may be compromised by the detected vibrations.

In some embodiments, system 10 can include a detector (e.g., detector 130 in FIG. 6) to measure the optical power of radiation 12 generated by source 30. Detector 130 can be connected to, or contained within, source 30, as shown in FIG. 6, or implemented as a separate device within system 10. In certain embodiments, for example, a beamsplitter or fiber splice within source 30 delivers a small fraction of radiation 12 to detector 130, which measures the power of the fraction of radiation. Processor 50 receives this measurement from detector 130, and uses calibration information to determine the optical power of each laser shot produced by source 30.

Processor 50 can take a variety of actions if the optical power of one or more shots is too large or too small (e.g., varies by more than 20% from a nominal optical power setting). In some embodiments, processor 50 can deliver a warning message to a system operator that the output power of source 30 is fluctuating beyond tolerance. The message can include a suggestion that source 30 may require service. In certain embodiments, processor 50 can flag measurement results that correspond to shots for which the optical power is beyond tolerance levels. Processor 50 can also be configured to discard such measurements entirely as unreliable.

System 10 can generally be configured to save all system measurements automatically and/or continuously. System measurements can be recorded according to sample, lot, shift, operator identity, time, and/or test performed, and can be directly linked or referenced to data files corresponding to particular samples. Measurements that can be recorded include, but are not limited to, detected vibrations, optical shot power, temperature, humidity, and pressure. In addition, sample measurements that are performed when a cleaning shot of radiation is delivered from source 30 to sample 20 can also be recorded. In some embodiments, these measurements can be saved together with the measurement data from one or more samples in a standard data structure (e.g., an XML structured file) for later analysis. The data structure can be extensible in scope to allow additional data types and sub-structures to be included. The data structure can then be processed by other software applications to extract useful information, including any of the different types of information disclosed herein.

In some embodiments, system 10 can include a configurable user interface that permits a system operator to selectively extract any of the information stored in the data structure described above. For example, the user interface allows the operator to extract data measured at certain times, under certain conditions, and/or for certain samples. The user interface also allows the operator to perform operations such as averaging certain portions of the data, integrating certain portions of the data, and discarding certain portions of the data, according to user-defined criteria.

As part of the calibration routines described herein, in some embodiments, processor 50 can be configured to perform a spectral calibration of detector 40. Processor 50 can determine that spectral calibration is advisable after a certain elapsed period of time, after a certain duration of usage of system 10, after a certain number of samples have been analyzed by system 10, and/or following detection of one or more triggering events such as a collision detection signal from sensor 89 and/or 99, and/or a measurement of average maximum signal intensity that falls below a threshold value, as described above.

Spectral calibration can be performed in a variety of ways. For an operator-assisted calibration, processor 50 can deliver a message to the system operator that spectral calibration should be performed, and can provide instructions to the operator to guide him or her through the calibration process. In some embodiments, the calibration process can be automated. For example, referring to FIG. 7, system 10 can initiate spectral calibration by de-activating source 30 (so that no emitted radiation 14 enters measurement port 92), and activating calibration source 93, so that light from calibration source 93 is coupled into detector 40 through calibration port 94. Processor 50 can then adjust the detection wavelength settings of detector 40 to match the known emission wavelength(s) of calibration source 93. The calibration information from manual or automated calibration can automatically be stored by system 10 and linked or referenced to subsequent measurement data for samples. System 10 can also permit parameters such as the frequency with which calibration routines are executed to be adjusted by the system operator.

In some embodiments, system 10 includes a housing 200 that encloses the system components. In general, housing 200 can be formed from a variety of materials, such as various metals and plastics. Typically, housing 200 is formed from an opaque material that blocks scattered radiation from passing through housing 200. In certain embodiments, housing 200 includes an optically transparent (or partially transparent) window 202 that allows a system operator to view the components of system 10 from outside housing 200. In some embodiments, housing 200 also includes an access panel 204 that can be readily removed to allow a system operator or service technician to access the components of system 10 without fully disassembling housing 200.

The contents of all publications cited herein (e.g., patents, patent application publications, and articles) are hereby incorporated by reference in their entirety.

EXAMPLES

The following examples are illustrative and not intended to be limiting.

Example 1

Two hundred and seventy beryl (Be₃Al₂Si₆O₁₈) crystals (var. emeralds) from 9 different locations in 8 different countries were analyzed using Laser Induced Breakdown Spectroscopy (LIBS). Thirty individual crystals from each location were studied. The countries where beryl samples were obtained are Afghanistan (AFG), Brazil (BRA), Colombia (COL), Mozambique (MOZ), Pakistan (PAK), South Africa (ZAF), Zambia (ZMB) and Zimbabwe (ZWE). Two separate and unique deposits in Colombia were analyzed in this study.

A Photon Machines Insight LIBS system was used for this study. Emission spectra from 30 laser excitations (shots) were collected at 30 unique excitation locations on the surface of each sample. For each excitation location analyzed, a single cleaning shot (an excitation on the sample surface without the emission generated from the excitation being collected) was performed prior to the collection shot (an excitation on the sample surface from which the emission generated from the excitation is collected). Sample ablation for the experiment reported here was achieved using a Nd:YAG laser operating at 266 nm with a repetition rate of 1 Hz, a typical pulse energy of 13 mJ and a pulse width of about 6 ns. The laser beam was focused onto the surface of the sample. A flow of 99.9% pure argon covered the surface of the sample to reduce contamination from ambient air. A second lens was used to collect the emission from the laser induced plasma via a fiber optic cable coupled to an Echelle spectrometer with a spectral resolution of 0.02 nm and a spectral range of 200.02-1000.02 nm (40,000 channels). At a delay of 1 μs after the laser pulse, the dispersed emission was recorded for a duration of 10 μs by an Intensified Charge-Coupled Device (ICCD) at a gain setting of 150 μs. The emission spectrum for each shot was saved independently, without averaging, to an electronic processer using the Chromium software that came as a part of the Photon Machines Insight LIBS system.

After data were collected on all 270 beryl samples using the experimental setup mentioned above, a blind test was performed. The data were analyzed using both an inventive method described herein (“the M2S method”) and a conventional partial least squares discriminant analysis (PLSDA). The PLSDA analysis was performed by a private third party (PTP). The PTP was not instructed to use PLSDA but to use any of the traditional analysis techniques typically used in the evaluation of LIBS data. The PTP is well respected and known to those in the art. The PTP was selected because they had worked previously on geological material determination problems containing far less robust data sets and they are considered experts in the field of “complex” LIBS data analysis.

Both the PTP and the M2S group were provided identical raw spectral data from the LIBS analysis. The PTP used only “every 5^(th) wavelength” in the PLSDA (disregarding 80% of the data available for analysis.) A simple 50/50 split of the data into separate sets for training and evaluation was used by the PTP. The PTP created a reference library using data from 15 samples from each site. The blind samples (containing data from the 15 remaining samples) were tested and matched to the samples in the reference library. It was determined by the PTP that performance of the predictive model generated using these data peaked around 20 latent variables.

The group using the M2S method processed all of the data provided in the method described earlier. Specifically, the raw spectral data for each sample (comprising 40,000 channels), be it a reference sample or an unknown sample, was converted into sequences of scaled spectra using the method described earlier in this application. The reference library consisted of the sequences of the scaled spectra of samples from known origins.

Each sequence for an unknown sample was compared to every known sequence in the reference library using a 270-fold leave one sample (sequence) out test design. A leave one out methodology begins by removing the first sample's sequences from the reference library and recreating the reference library from the sequences of the remaining 269 samples. This process was repeated for every sample in the test.

The comparison was made based on a weighted K-nearest neighbor algorithm which produced a table containing the distance between the unknown samples sequences and every known sequence in the reference library. The table related the distance between the unknown sample and all of the known samples based on the distance between their respective sequences of scaled spectra. The table was ordered from the smallest distance to the largest distance from the unknown sample's sequences to the known samples' sequences. Subsequently, each distance was used to compute a score, which was based on the relationship of the distance between the unknown and known sequences of scaled spectra. The smaller the distance between the unknown and known sequences of scaled spectra, the greater the value of the score. This is known as the weighting function. A second table was created that related the score that each known sample received. This table was ordered from the largest score to the smallest score. The known sample with the highest score was identified as the closest match to the unknown sample. Thus, the origin of the known sample identified as having the closest match was assigned to the unknown sample.

The accuracy of the matching in the M2S and PTP groups is summarized in Table 1 below.

TABLE 1 Country M2S Method PLSDA AFG 98.0% 80.0% BRA 98.0% 58.0% COL 100.0%  97.3% MOZ 98.0% 88.7% PAK 98.0% 48.7% ZAF 99.0% 96.0% ZMB 95.0% 98.7% ZWE 97.0% 44.0% Average   98%   76%

As shown in Table 1, the M2S method provided an average of 98% accuracy in determining country of origin, while PLSDA provided an average accuracy of only 76%.

After the conclusion of the study, the PTP was asked why they had disregarded 80% of the data. The PTP indicated that an excessive amount of data had been provided and that using any conventional data analysis techniques with that much data would require too much processing time.

Example 2

90 coupons of 17-4 Stainless Steel, all initially originating from the same bar, but with three different conditions (heat treatments), were analyzed using Laser Induced Breakdown Spectroscopy (LIBS). Thirty individual coupons from each treatment were studied. The treatments studied were Condition A; Condition H900, and Condition H1150. The material analyzed in this study did not come with certificates, thus the specifics of the heat treatment can not be certified. In general the following is true for each of the conditions studied.

Condition A is the original annealed condition for the bar, no heat treatment or aging. Condition H900 was age hardened at 482° C. for 1 hour, and then air cooled. Condition H1150 was heated at 760° C. for 2 hours and air cooled, then heated at 621° C. for 4 hours, and then air cooled.

A Photon Machines Insight LIBS system was used for this study. Emission spectra from 64 laser excitations (shots) were collected at 64 unique excitation locations on the surface of each sample. For each excitation location analyzed, a single cleaning shot (an excitation on the sample surface without the emission generated from the excitation being collected) was performed prior to the collection shot (an excitation on the sample surface from which the emission generated from the excitation is collected). Sample ablation for the experiment reported here was achieved using a Nd:YAG laser operating at 1064 nm with a repetition rate of 1 Hz, a typical pulse energy of 90 mJ and a pulse width of 6 ns. The laser beam was focused onto the surface of the sample. A flow of 99.9% pure argon covered the surface of the sample to reduce contamination from ambient air. A second lens was used to collect the emission from the laser induced plasma via a fiber optic cable coupled to an Echelle spectrometer with a spectral resolution of 0.02 nm and a spectral range of 200.02-1000.02 nm (40,000 channels). At a delay of 1.25 μs after the laser pulse, the dispersed emission was recorded for a duration of 10 μs by an Intensified Charge-Coupled Device (ICCD) at a gain setting of 200 μs. The emission spectrum for each shot was saved independently, without averaging, to an electronic processer using the Chromium software that came as a part of the Photon Machines Insight LIBS system.

Using the experimental setup described above, each sample was analyzed 64 times; for a total of 1920 collection shots for each condition. A total of 5760 collection shots across all three conditions were collected. The raw spectral data for each sample (comprising 40,000 channels), was converted into sequences of scaled spectra using the method described earlier in this application. The reference library consisted of the sequences of the scaled spectra of samples.

Each sequence for an unknown sample was compared to every known sequence in the reference library using a 90-fold leave one sample (sequence) out test design. A leave one out methodology begins by removing the first sample's sequences from the reference library and recreating the reference library from the sequences of the remaining 89 samples. This process was repeated for every sample in the test.

The comparison was made based on a weighted K-nearest neighbor algorithm which produced a table containing the distance between the unknown samples sequences and every known sequence in the reference library. The table related the distance between the unknown sample and all of the known samples based on the distance between their respective sequences of scaled spectra. The table was ordered from the smallest distance to the largest distance from the unknown sample's sequences to the known samples' sequences. Subsequently, each distance was used to compute a score, which was based on the relationship of the distance between the unknown and known sequences of scaled spectra. The smaller the distance between the unknown and known sequences of scaled spectra, the greater the value of the score. This is known as the weighting function. A second table was created that related the score that each known sample received. This table was ordered from the largest score to the smallest score. The known sample with the highest score was identified as the closest match to the unknown sample. Thus, the Condition of the known sample identified as having the closest match was assigned to the unknown sample.

TABLE 2 Condition of 17-4 Stainless Steel M2S Method Condition A 96.7% Condition H900 98.9% Condition H1150 97.8% Average 97.8%

As shown in Table 2, the M2S method provided an average of 97.8% accuracy in determining the condition of 17-4 stainless steel.

As discussed earlier, peaks in the data were initially thought to merely represent atomic emissions. However, it has now been shown that peaks not only contain atomic emissions, but also represent isotopic, molecular, and molecular isotopic emissions. Some data in the peaks may be produced by spectral interference. For example the concentrations of Mg and Na may not be accurate quantitative values. Rather, due to the interaction in the plasma, some concentrations of one element, say Na, appear to be greater than what is actually present, and Mg appears to have a lower concentration than what is actually present, due to the spectral interference phenomenon. Moreover, the shape of the peaks is critical, because the shape may represent re-absorption of the emitted element. This is ordinarily seen as a flat top of a peak rather than a point.

FIG. 5 displays three graphs (Q-set). Each graph is a visual representation of the different members of the sequence that were found in a sample of the Condition H900 17-4 Stainless Steel. A magnified view of approximately 1600 channels of the spectrum of the sequence member is provided to the right in a circle. Each area magnified is the same section of the sequence. The top graph represents the Prime-Q (2Qn4), this is the primary constituent found in all of the data for the 17-4 Stainless Steel. The second graph (2Qn2) and the third graph (2Qn7) represent two other members of the sequence for the Condition H900 samples.

Across all three conditions, a total of 9 different members of the sequence were seen. Table 3 shows the frequency of each condition. Only three members of the sequence were seen in any one sample at any given time.

TABLE 3 2Qn4 Condition 2Qn1 2Qn2 2Qn3 (Prime-Q) 2Qn5 2Qn6 2Qn7 2Qn8 2Qn9 A 0 11 21 1847 20 14 7 0 0 H900 4 23 13 1847 15 2 9 5 2 H1150 15 15 6 1848 13 2 6 8 7 Total # of 19 49 40 5542 48 18 22 13 9 Occurrences

In the case of the Condition A samples, the primary constituent (2Qn4) appeared 5542 out of 5760 times. The 2Qn1, 2Qn8, and 2Qn9 constituents did not appear at all in the Condition A samples. The 2Qn2 constituent appeared 11 times, the 2Qn3 appeared 21 times, the 2Qn5 constituent appeared 20 times, the 2Qn6 constituent appeared 14 times, and the 2Qn7 constituent appeared 7 times.

In the case of the Condition H900 samples and the Condition H1150 samples, the sequence of spectra produced the same members: 2Qn1, 2Qn2, 2Qn3, 2Qn4, 2Qn5, 2Qn6, 2Qn7, 2Qn8, and 2Qn9. However, the probability distribution for these members differed from that obtained from the Condition A sample. Specifically, the 2Qn1, 2Qn8, and 2Qn9 constituents were found in the Condition H900 (4 occurrences) and the Condition H1150 (15 occurrences) samples but were absent from the Condition A samples. The 2Qn2 constituent appeared 23 out of 5760 times in Condition H900 and 15 out of 5760 times in Condition H1150. The 2Qn3 constituent appeared 13 out of 5760 times and 2Qn4 appeared 1847 out of 5760 times in Condition H900. In this same condition, the 2Qn5 constituent appeared 15 out of 5760 times, 2Qn6 appeared 2 out of 5760 times, 2Qn7 appeared 9 out of 5760 times, 2Qn8 appeared 5 out of 5760 times, and 2Qn9 appeared 2 out of 5760. In the case of the Condition H1150 samples, the sequence of spectra produce the same primary constituent, 2Qn4, which appeared 1848 out of 5760 times. The 2Qn1 constituent appeared 15 out of 5760 times and 2Qn2 appeared 15 out of 5760 times. In this same condition, the 2Qn3 constituent appeared 6 out of 5760 times and 2Qn5 appeared 13 out of 5760. The 2Qn6 and 2Qn7 constituents appeared 2 and 6 out of 5760 times, respectively, and the 2Qn8 and 2Qn9 constituents appeared 8 and 7 out of 5760 times, respectively. By comparing the probability distribution of the member for all three types of samples, the samples could be distinguished among one another as set forth in Table 2 above.

Referring again to FIG. 5, five unique areas within the magnified area have been selected and highlighted. These areas are meant to help visually display the differences in the data that the algorithm identifies. One will notice that the location, size, and shapes of the peaks in each of these three boxes are unique, and the algorithm is able to identify these differences. In Box One of each of the three graphs, one will notice that the height of the first peak in the bottom graph (2Qn7) is greater than that of the same peak in graphs 2Qn4 and 2Qn2. The second peak in Box One is highest in the first graph (2Qn4) and almost non-existent in the third (2Qn7). As one continues to visually inspect the data, numerous differences can be seen. These differences characterize each member of the sequence.

Other Embodiments

A number of embodiments have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other embodiments are within the scope of the following claims. 

1. A method for analyzing a sample, the method comprising: exposing the sample to plurality of pulses of electromagnetic radiation to convert a portion of the sample into a plasma; recording a spectrum of electromagnetic radiation emitted in response to each of the plurality of pulses to define a sequence of spectra for the sample, wherein each member of the sequence corresponds to the spectrum recorded in response to a different one of the pulses; using an electronic processor to compare the sequence of spectra for the sample to a sequence of spectra for each of multiple reference samples in a reference library; and using the electronic processor to determine information about the sample based on the comparison to the multiple reference samples in the library, wherein exposing the sample to the plurality of pulses of electromagnetic radiation comprises directing the pulses to be incident on different spatial regions of the sample, and wherein a temporal delay between exposing the sample to each successive radiation pulse is constant.
 2. The method of claim 1, wherein the plurality of pulses of electromagnetic radiation form a hexagonal array on the sample.
 3. The method of claim 1, wherein the plurality of pulses of electromagnetic radiation form an array of equally spaced exposure regions on the sample.
 4. The method of claim 3, wherein the exposure regions are positioned along a circumference of a common circle.
 5. The method of claim 1, wherein the plurality of pulses comprises 60 or more pulses.
 6. The method of claim 1, further comprising selecting the temporal delay between successive radiation pulses by adjusting a spacing between the different spatial regions of the sample.
 7. The method of claim 6, further comprising selecting the temporal delay based on the sample.
 8. The method of claim 6, further comprising selecting a power of each of the plurality of radiation pulses.
 9. The method of claim 6, further comprising measuring a first sequence of spectra for the sample corresponding to a first constant temporal delay between successive radiation pulses, and measuring a second sequence of spectra for the sample corresponding to a second constant temporal delay between successive radiation pulses that is different from the first constant temporal delay.
 10. The method of claim 9, further comprising determining the information about the sample based on the first and second sequences of spectra.
 11. A system for analyzing a sample, the system comprising: an electromagnetic radiation source configured to expose the sample to plurality of pulses of electromagnetic radiation to convert a portion of the sample into a plasma; a detector configured to record a spectrum of electromagnetic radiation emitted in response to each of the plurality of pulses to define a sequence of spectra for the sample, wherein each member of the sequence corresponds to the spectrum recorded in response to a different one of the pulses; and an electronic processor configured to: control the radiation source so that the plurality of electromagnetic radiation pulses are incident on different spatial regions of the sample, wherein a temporal delay between exposing the sample to each successive radiation pulse is constant; compare the sequence of spectra for the sample to a sequence of spectra for each of multiple reference samples in a reference library; and determine information about the sample based on the comparison to the multiple reference samples in the library.
 12. The system of claim 11, wherein the electronic processor is configured to control the radiation source so that the plurality of pulses of electromagnetic radiation form a hexagonal array on the sample.
 13. The system of claim 11, wherein electronic processor is configured to control the radiation source so that the plurality of pulses of electromagnetic radiation form an array of equally spaced exposure regions on the sample.
 14. The system of claim 13, wherein the exposure regions are equally spaced along a circumference of a circle.
 15. The system of claim 11, wherein the plurality of pulses comprises 60 or more pulses.
 16. The system of claim 11, wherein the electronic processor is configured to select the temporal delay between successive radiation pulses by adjusting a spacing between the different spatial regions of the sample.
 17. The system of claim 16, wherein the electronic processor is configured to select the temporal delay based on the sample.
 18. The system of claim 16, wherein the electronic processor is configured to control the radiation source to select a power of each of the plurality of radiation pulses.
 19. The system of claim 16, wherein the electronic processor is configured to: use the detector to measure a first sequence of spectra for the sample corresponding to a first constant temporal delay between successive radiation pulses; and use the detector to measure a second sequence of spectra for the sample corresponding to a second constant temporal delay between successive radiation pulses that is different from the first constant temporal delay.
 20. The system of claim 19, wherein the electronic processor is configured to determine the information about the sample based on the first and second sequences of spectra. 